Tail Optimality and Performance Analysis of the Nudge-M Scheduling Algorithm

TL;DR

This paper introduces Nudge-$M$ scheduling, an extension of the Nudge algorithm, providing asymptotic optimality and explicit performance bounds for light-tailed job size distributions, with practical computation methods for response times.

Contribution

It proposes Nudge-$M$ scheduling, proving its asymptotic optimality and deriving explicit tail improvement ratios and response time analysis methods.

Findings

Nudge-$M$ achieves asymptotic optimality among Nudge algorithms.

Explicit formulas for tail improvement ratio (ATIR) are derived.

Numerical methods for response time distribution with phase-type job sizes are presented.

Abstract

Recently it was shown that the response time of First-Come-First-Served (FCFS) scheduling can be stochastically and asymptotically improved upon by the {\it Nudge} scheduling algorithm in case of light-tailed job size distributions. Such improvements are feasible even when the jobs are partitioned into two types and the scheduler only has information about the type of incoming jobs (but not their size). In this paper we introduce Nudge-$M$ scheduling, where basically any incoming type-1 job is allowed to pass any type-2 job that is still waiting in the queue given that it arrived as one of the last $M$ jobs. We prove that Nudge-$M$ has an asymptotically optimal response time within a large family of Nudge scheduling algorithms when job sizes are light-tailed. Simple explicit results for the asymptotic tail improvement ratio (ATIR) of Nudge-$M$ over FCFS are derived as well as explicit results for the optimal parameter $M$. An expression for the ATIR that only depends on the type-1 and type-2 mean job sizes and the fraction of type-1 jobs is presented in the heavy traffic setting. The paper further presents a numerical method to compute the response time distribution and mean response time of Nudge-$M$ scheduling provided that the job size distribution of both job types follows a phase-type distribution (by making use of the framework of Markov modulated fluid queues with jumps).